{"id":"W2963642948","doi":"","title":"Synthesizing Programs for Images using Reinforced Adversarial Learning.","year":2018,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Discriminator; MNIST database; Artificial intelligence; Inference; Machine learning; Graphics; Rendering (computer graphics); Adversarial system; Deep learning; Generative grammar; Generative adversarial network; Computer graphics; Reinforcement learning; Computer graphics (images)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005300883,0.0002485721,0.0002263402,0.0001798449,0.0005287972,0.0006046513,0.0008753447,0.0000836594,0.0001884722],"category_scores_gemma":[0.0007333916,0.0002317691,0.0001415813,0.0001775719,0.000116326,0.0006332155,0.000237886,0.0003808933,0.00006229667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008427634,"about_ca_system_score_gemma":0.00009420902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001376052,"about_ca_topic_score_gemma":0.00001047912,"domain_scores_codex":[0.9981887,0.0001767975,0.0003117757,0.0005432389,0.0003973107,0.0003822035],"domain_scores_gemma":[0.9986196,0.0002197903,0.0002696244,0.0002231532,0.000574885,0.0000929849],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006843416,0.0001384283,0.003240314,0.00003188481,0.0004541664,0.00002880536,0.002074343,0.1221491,0.09787249,0.2199061,0.0004465766,0.5529735],"study_design_scores_gemma":[0.000515249,0.0004224121,0.00007943692,0.00008927421,0.00001233493,0.000008254802,0.00006592879,0.9772995,0.007825404,0.0008081389,0.01259997,0.0002741322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003764433,0.00001768614,0.9810079,0.001101321,0.00112136,0.0002674897,0.000003566586,0.0002314233,0.01248483],"genre_scores_gemma":[0.9132045,0.00001249231,0.08330794,0.0001541431,0.001010512,0.00002444041,0.00002643631,0.00002358437,0.002235936],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9094401,"threshold_uncertainty_score":0.9451271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05329083591455927,"score_gpt":0.3083080371160244,"score_spread":0.2550172012014652,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}